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Record W2752534460 · doi:10.1002/ajmg.a.38462

FOXP1 haploinsufficiency: Phenotypes beyond behavior and intellectual disability?

2017· article· en· W2752534460 on OpenAlexafffund
Angela Myers, Christèle du Souich, Connie Yang, Lior Borovik, Jill Mwenifumbo, Rosemarie Rupps, Causes Study, Anna Lehman, Cornelius F. Boerkoel

Bibliographic record

VenueAmerican Journal of Medical Genetics Part A · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Neurodevelopmental Disorders
Canadian institutionsBC Children's HospitalChildren's & Women's Health Centre of British ColumbiaUniversity of British Columbia
FundersProvincial Health Services Authority
KeywordsHaploinsufficiencyPhenotypeIntellectual disabilityGeneticsMedicineBiologyPsychologyGene

Abstract

fetched live from OpenAlex

The forkhead box (FOX) transcription factors have roles in development, carcinogenesis, metabolism, and immunity. In humans FOXP1 mutations have been associated with language and speech defects, intellectual disability, autism spectrum disorder, facial dysmorphisms, and congenital anomalies of the kidney and urinary tract. In mice, Foxp1 plays critical roles in development of the spinal motor neurons, lymphocytes, cardiomyocytes, foregut, and skeleton. We hypothesized therefore that mutations of FOXP1 affect additional tissues in some humans. Supporting this hypothesis, we describe two individuals with novel variants of FOXP1 (NM_032682.5:c.975-2A>C and NM_032682.5:c.1574G>A) and additional features. One had a lung disease resembling neuroendocrine cell hyperplasia of infancy (NEHI), and the second had a skeletal disorder with undertubulation of the long bones and relapsing-remitting fevers associated with flushing and edema. Although attribution of these traits to mutation of FOXP1 requires ascertainment of additional patients, we hypothesize that the variable expression of these additional features might arise by means of stochastic developmental variation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.791
Threshold uncertainty score0.595

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.285
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations29
Published2017
Admission routes2
Has abstractyes

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Same venueAmerican Journal of Medical Genetics Part ASame topicGenetics and Neurodevelopmental DisordersFrench-language works237,207